five

Global Financial Inclusion (Global Findex) Database 2021 - Costa Rica

收藏
microdata.worldbank.org2022-12-16 更新2025-01-22 收录
下载链接:
https://microdata.worldbank.org/index.php/catalog/4630
下载链接
链接失效反馈
官方服务:
资源简介:
Abstract --------------------------- The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds. The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments. The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals. Geographic coverage --------------------------- National coverage Analysis unit --------------------------- Individual Kind of data --------------------------- Observation data/ratings [obs] Sampling procedure --------------------------- In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender. In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used. The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent). For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day. Sample size for Costa Rica is 1001. Mode of data collection --------------------------- Landline and mobile telephone  Research instrument --------------------------- Questionnaires are available on the website. Sampling error estimates --------------------------- Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

摘要 --------------------------- 第四版全球金融包容指数报告揭示了在COVID-19疫情期间,人们在流动性限制和健康政策推动下如何获取和使用各类金融服务的情况。 全球金融包容指数是世界上最全面的金融包容性数据库。它也是唯一允许进行全球和区域间跨国分析的需求侧数据来源,能够提供严谨且多维度的视角,展示成年人如何储蓄、借款、支付以及管理金融风险。 2021年的全球金融包容指数数据来自对超过120个经济体中约128,000名成人的国家代表性调查。本版报告继2011年、2014年和2017年版之后,新增了一系列衡量金融健康和弹性的指标,并包含了关于数字支付采用情况的更细粒度数据,包括商家和政府支付。 全球金融包容指数是金融服务从业者、政策制定者、研究人员和发展专业人士不可或缺的资源。 地理覆盖范围 --------------------------- 国家覆盖 分析单元 --------------------------- 个人 数据类型 --------------------------- 观察数据/评级 [obs] 抽样程序 --------------------------- 在大多数发展中经济体中,全球金融包容指数数据传统上通过面对面访谈收集。在电话普及率低于人口80%或面对面调查是常规方法的经济体中,调查以面对面方式进行。然而,由于持续的COVID-19相关流动性限制,2021年这些经济体中的一些无法进行面对面访谈。因此,在2017年曾进行面对面调查的67个经济体中进行了基于电话的调查。这些67个经济体根据人口规模、电话普及率、COVID-19感染率和在Gallup本应进行面对面数据收集的地方执行基于电话方法的可行性而被选中。在整个访谈过程中,Gallup考虑了移动电话和固定电话的拥有情况。根据2019年Gallup世界民意调查数据,在这些经济体中最后一次进行面对面调查时,几乎所有地区的成年人中至少有80%报告拥有移动电话。所有样本都是概率性的,并且在全国范围内代表居住成人人口。在17个之前曾参与全球金融包容指数调查的经济体中,由于移动电话拥有率低和调查限制,电话调查不是一个可行的选择。这些经济体将在2022年收集数据,并在2023年发布。 在面对面调查进行的经济体中,抽样第一阶段是识别主要抽样单位。这些单位按人口规模、地理区域或两者结合进行分层,通过一个或多个抽样阶段实现聚类。如果可用人口信息,样本选择基于与人口规模成比例的概率;否则,使用简单随机抽样。使用随机路线程序选择抽样家庭。除非有明确的拒绝,否则调查员最多尝试三次对抽样家庭进行调查。为了增加接触和完成调查的概率,在不同时间尝试,并在可能的情况下,在不同天进行尝试。如果无法在初始抽样家庭中获得访谈,则使用简单替代方法。在选定的家庭内随机选择受访者。列出所有符合条件的家庭成员,手持调查设备随机选择要访谈的家庭成员。对于纸质调查,使用Kish网格法选择受访者。在文化限制规定性别匹配的经济体中,从所有符合条件的访谈者性别成年人中随机选择受访者。 在传统上基于电话的经济体中,受访者选择遵循与往年相同的程序,使用随机数字拨号或国家代表性的电话号码列表。在大多数移动电话和固定电话普及率高的经济体中,使用双重抽样框架。 对新的基于电话的经济体应用相同的受访者选择程序。在固定电话存在和使用率基于历史Gallup估计为20%或更高的经济体中,使用双重框架(固定电话和移动电话)随机数字拨号。在固定电话存在率有限或没有的经济体中,使用移动电话随机数字拨号。 在移动电话或固定电话普及率80%或更高的经济体中,固定电话受访者的随机选择是通过使用最新生日或家庭编号方法实现的。在这些经济体中或移动电话或固定电话普及率低于80%的经济体中,移动电话受访者的进一步选择不需要进行。在每个家庭中至少尝试三次联系一个人,分布在不同的天和不同的时间。 哥斯达黎加的样本量为1001。 数据收集方式 --------------------------- 固定电话和移动电话 研究工具 --------------------------- 问卷可在网站上找到。 抽样误差估计 --------------------------- 标准误差的估计(考虑抽样误差)因国家和指标而异。有关特定国家的误差范围,请参阅方法论部分和相关表格,Asli Demirgüç-Kunt, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. 全球金融包容指数数据库2021:COVID-19时代的金融包容性、数字支付和弹性。华盛顿特区:世界银行。
提供机构:
microdata.worldbank.org
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作